Table Of Contents

12. Applications of Cyclostationarity to Signals Intelligence

  • 12.1 Descriptive Material on SSPI

    Compared with the Nation’s largest defense contractors, the creativity and productivity in development of theory, method, algorithms, and software of the tiny R&D firm, SSPI
    (Statistical Statistical | adjective Of or having to do with Statistics, which are summary descriptions computed from finite sets of empirical data; not necessarily related to probability. Signal Processing, Inc.), in the field of signals intelligence is nothing short of phenomenal. The SSPI team was comprised of fewer than ten employees, most of whom were trained by Professor Gardner, SSPI’s president and chief scientist, as PhD students and post-doctoral researchers at the University of California, Davis. The entire budget of SSPI over its 25-year lifetime was less than $25M. The company’s huge store of intellectual property was sold (in essence, gifted) in 2011 to Lockheed Martin Corporation at the time of Gardner’s retirement for a small fraction of this sum and an agreement for placement of SSPI’s senior engineers at the Lockheed Martin Advanced Technology Center in Palo Alto, California.

    The following images were excerpted from a 2003 SSPI PowerPoint presentation. They provide a concise overview of some of the technology that was developed by SSPI during the first 15 years of its 25-year history from 1986 to 2011. Following this presentation is an in-depth summary of SSPI’s technology development during its final 10 years. Also available herein on Page 6 is an in-depth survey of SSPI’s early work on application of cyclostationarity theory and method to the specialized field of signal interception. This survey was written 5 years after the 1987 publication of the enabling book [Bk2]. 

    2010 Summary of SSPI’s Technology Development Activities

    Following is an outline of SSPI’s developed technology in the area of communications signal processing specifically for unintended receivers, as of the end of 2010. The great majority of SSPI’s technology is in the form of technical documents and software (scientific/engineering documentation of innovative theory, methodology, algorithms, software implementations of individual signal processing algorithms and data processing systems of such algorithms, and performance-evaluation data and analysis). These theoretical and methodological results were developed without outside funding. The sources of funding include investments-in-kind of the Owner’s labor, SSPI IRAD funding not billed to any clients as an overhead expense, and some SSPI IRAD funding billed as an overhead expense to contracting customers. Following this outline, a more detailed summary of achievements and status is provided.

        1. Novel (and deeper than state-of-the-art) theoretical problem reformulation and solution for long-coherent-integration for fixed or moving ground-based (or low-altitude) RF-Emitter detection/location from primarily aircraft and satellites, based on:
          1. High-fidelity mathematical modeling of
            1. Communications signals of interest
            2. Collection uplink/downlink channels, uplink transmit/receive antennas, multi-path propagation, blocked line of sight
          2. Mathematical optimization of statistical performance criteria for geolocation of known-, partially-known-, and unknown-signal emitters
          3. Solution for novel explicit statistical location-performance-reporting and performance-prediction formulas
        2. Translation of mathematical solutions into novel signal-processing algorithms, implementable in S/W & H/W, configurable according to application and scenario
        3. Translation of mathematical solution into a novel theory of statistically optimum, passive, evolutionary Bayesian Aperture Synthesis for Emitter Location (BASEL), including novel engineering concepts:
          1. Posterior Probability Density Images and Likelihood Images in three dimensions
          2. Formulas for RF Imaging Point-Spread Functions for amplitude and energy
        4. Mathematical unification, as well as extension/generalization, of diverse technologies into a single cohesive methodology that includes:
          1. Optimum Joint multi-sensor TDOA/FDOA/AOA estimation
          2. VLBI types of RF Imaging from overhead
          3. Signal Selective RF Imaging
          4. RF Imaging that suppresses reflector positions
          5. RF Imaging that effectively sees through blocked line of sight
        5. Beta prototype S/W for configurable processor that performs long-coherent-integration geolocation and performance prediction for fixed emitters
        6. In a 9 November 2010 US Government briefing to contractors, the following Government’s Vision for SSPI’s Future Role in Communications Monitoring Technology Transition into the planned Service Oriented Architecture was presented:
          1. Cross-mission data partitioning/conditioning service
          2. Synthetic signal generation service
          3. RCAF computation/geo-registration service
          4. RCAF/CAF combination/fusion service
          5. Multipath measurement service
          6. Confidence region generation service

          These six services represent the Government’s projections for SSPI products to be installed in Government Monitoring Service Delivery Systems.

        1. Novel cyclostationarity-exploiting algorithms and underlying theory for signal detection and estimation
        2. Cochannel-interfering-signal classification algorithms and theory
        3. Beta prototypes of algorithms in S/W, plus extensive documentation
        1. Novel extensions/generalization of Viterbi algorithms for cochannel signals
        2. Novel Viterbi algorithms for cochannel DQAM signals with distinct constellations and symbol rates
        3. Beta prototypes of algorithms in S/W, plus documentation

    Background & Challenge

    The substantial body of knowledge, experience, and technology that is the standard throughout the monitoring Community for producing emitter-location estimates and calculating approximate confidence regions (or percent containment regions) for emitter location estimates was developed specifically for radar emitters that transmit strong signals relatively frequently from the same location, enabling collectors to make many statistically-independent measurements over time and produce many statistically-independent estimates of emitter location.  

    In contrast, most uplink multi-user communications emitters are mobile, do not remain at the same location for long, transmit weak signals and only sporadically.  In addition, legacy Radar location systems were designed for pulsed Radars and extract TDOA/FDOA measurements before processing for location, whereas communications emitters are typically of the continuous-wave type (digital or analog modulation), not pulsed; so, the original justification for basing all processing for location on TDOA/FDOA measurements typically does not apply to communications-emitter location. Furthermore, communications emitters are typically corrupted by comparable strength cochannel interference, which is relatively unusual for Radar emitters, especially at the densities of interferers encountered with communications emitters.

    Despite these fundamental differences between the two classes of emitter-location problems, the successes of the technology developed for radars historically provided strong motivation for taking the approach, to the communications emitter problem of more recent interest, of adapting the older technology to the newer problem. 

    Yet, it is being increasingly recognized, as the nature of the communications emitter detection/location scenarios of interest evolve along with evolving communications technology, that the standard approximate containment regions can be highly inaccurate for communications-emitter scenarios in contrast with the level of accuracy demonstrated for Radar-emitter scenarios.

    Moreover, it has been found more recently that adapting technology from radio frequency emission Imaging (developed for various applications including Radio Astronomy Imaging) can result in important improvements in capability, such as higher detection sensitivity, higher precision, and higher spatial resolution of closely spaced cochannel emitters. This breakthrough has demonstrated that more explicit recognition of the fundamental differences between the two classes of emitter-location problems opens the door to more innovative solutions for communications emitter detection/location, confidence calculation and emitter identification.  The areas of technology development that are now widely recognized as needing new methodology include

        1. Fusion of data from more diverse RF sensors/apertures and other forms of monitoring data
        2. Signal Processing Algorithm and Processor development for more advanced detection/location capabilities
        3. Calculation-Algorithm development for more accurate location-estimation confidence reporting and prediction

    Response to the Challenge: Synthetic Aperture Zooming

    Given this widely recognized need for new methodology, SSPI has “gone back to basics” by achieving the following (as of 2010):

          1. Derived high-fidelity mathematical models of collected data from first principles of physics and collector-design, and corroborated these models with collected data from various types of collection platforms and apertures
          2.  Brought to bear the classical probabilistic theory of statistical inference and decision (both Bayesian and Maximum-Likelihood) to:
            1. derive statistically optimum (SO) detection/location solutions from the mathematical RF-data models
            2. translate these SO mathematical solutions into signal processing algorithms
          3. Applied this theory to:
            1. obtain exact mathematical expressions for Bayesian containment regions
            2. derive, from these mathematical expressions, algorithms for containment region computation

    The results of this theoretical work are believed to be revolutionary:

          • They bear little resemblance to theory and method developed for radar emitter location and transitioned to communications emitter detection/location
          • They provide a comprehensive unifying and mathematically tractable theoretical framework for understanding, evaluating, and comparing emerging technology, such as:
              • traditional radar detection/location techniques as applied to communication emitters,
              • very-long-baseline/long-coherent-integration interferometric techniques,
              • passive synthetic aperture detection/location techniques
              • unstable-sensor-phase-compensating techniques for long coherent integration, and
              • SO techniques developed at SSPI which subsume the above
          • This framework reveals that the SO processors that perform long coherent integration with moving sensors implement statistically optimum versions of or alternatives to the above-listed existing techniques, and the theory provides explicit formulas for the RF Images produced by electronically steered and optimized synthetic apertures (which are implemented by SSPI’s configurable BASEL processor).
          • The above has led to specific proposed techniques for enhancing existing systems and by improving performance and expanding capabilities.
          • This framework also reveals now to produce statistically optimum estimates of the tracks of moving emitters, using long coherent integration without incurring the blurring effect that occurs when existing methods of such integration are used to track emitters
          • The SO solutions reveal why legacy approximate percent-containment ellipses and ellipsoids are seriously mismatched to communications-emitter location estimates produced by emerging techniques, and they provide:
              • exact Bayesian containment regions (no approximations) that derive directly from the classical minimum-risk statistical theory based on posterior probability density functions
              • proof that the actual data collected is reflected in the exact containment regions in a much more substantial way compared with the standard results, which average over all hypothetical realizations of a stochastic process model
              • superior strategic techniques, e.g., for mission planning based on exact confidence calculations
              • superior tactical techniques, e.g., for determining exact CEP(50) (Circular Error Probability = ½) regions used by the military
              • Superior monitoring product updating with the acquisition of new data, using the theory of Bayesian Learning
          • The SO solutions provide a theoretical and algorithmic basis for leveraging prior information (tips) in a statistically optimum manner which results in minimum area exact containment regions.

    SSPI has developed a highly configurable architecture for implementing a general workhorse version of all the above, collectively referred to as the Bayesian Aperture Synthesis Emitter Location (BASEL) processor. The capability of the BASEL processor is reviewed in summary form on page 11.3.2.


    HBC Classifier Development

    The concept of the HOCS-based Classifier (HBC) system, in which cyclic cumulants were first proposed as uniquely qualified signal features for classification in cochannel interference, was introduced in 1991 by SSPI on a research project funded jointly by the Army Research Office and the National Science Foundation. (HOCS = Higher-Order Cyclostationarity.) SSPI continued to develop the HBC system for several years under contract with another Government agency. In this period, the HBC was made into a viable end-to-end, stand-alone software tool with band-of-interest detection, filtering, and sample rate conversion, HOCS detection and estimation, feature grouping, and automatic signal classification. Release 1.0 of the HBC was delivered to the Government in 1999. 

    In 1999, HBC development and evaluation continued under a contract with a different Government agency. In this contract the HBC was tested with real-world data sets and software modifications were made to improve its robustness and usefulness. SSPI delivered release 1.2 of the HBC by the end of 1999

    High-level capabilities include:

            • Single-Signal-in-Noise classification for a broad range of in-band SNRs for ~30 modulation types and pulse shapes including AM, FM, PSK, digital QAM, FSK, CPFSK, ASK, SQPSK, MSK, sine wave, OOK, and pi/4-DQPSK
            • Automatic detection and processing of signals in spectrally disjoint bands
            • Classification of cochannel signals (either of the same modulation type or different types)

    High-level limitations include:

            • For a given SINR, classification performance is highly dependent upon data-record length
            • Cannot detect non-cyclostationary signals, transient signals, strict-sense stationary signals (noise-like signals), and signals exhibiting any modulation type not in the library
            • The HBC system has not been used by SSPI in its entirety for the past 8 years and will require reconfiguration and testing of the SW for use in new applications

    CuHBC Classifier Development

    In 2000-2001 SSPI began development under IR&D of the Custom HBC (CuHBC) for environments with negligible cochannel interference. During this period an initial prototype of the classifier in MATLAB and C was found to offer better performance than the original HBC for cases with negligible cochannel interference but with impairments due to channel distortion found in real data. In 2001 SSPI received a subcontract from Science Applications International Corporation (SAIC) to continue the development of the CuHBC and test it on real data sets. Development continued on the CuHBC system through 2006. During this time, the digital PSK/QAM classification subsystem of CuHBC was integrated with two Government customer’s operational communications monitoring systems, under a contract through Raytheon State College.  

    High-level capabilities include:

            • Modulation types supported
              • Linear Digital: PSK2, PSK4, PSK8, DQPSK, SQPSK, QAM
              • FSK: BFSK, QFSK, M-FSK
            • Tolerant to channel distortion in received data
            • Successful classification for single signals in noise when data record includes as few as approximately 250 – 500 symbols, for in-band SNRs in the range 0dB to10dB (modulation type dependent)
            • Best performance achieved for linear digital modulation types

    High-level limitations include:

            • Analog modulations AM, FM, SSB not yet supported
            • Unable to classify in presence of CCI
            • Limited capabilities for FSK signals and higher-order QAM signals with arbitrary constellations
            • The CuHBC system in its entirety has not been used by SSPI since 2006 and requires reconfigure and testing of the SW for possible use in a new application

    Maximum-Likelihood Classifier Development

    During the period 2004 – 2006, SSPI worked under two separate subcontracts with Bit-Systems and Raytheon State College (prime contracts with Government customers) to develop maximum-likelihood classification techniques for signals utilizing M-FSK and QAM modulations  Delivered algorithms were integrated into operational system upgrades by RSC as part of a massive upgrade performed by BITS

    FSK Classification.  A general approach has been developed to perform optimum FSK signal-modulation-classification and signal-parameter-estimation based on the maximum-likelihood principle.  Two specific algorithms were developed including (1) ML-FSK Baud-Rate Estimation for M-FSK signals in harsh environments (e.g., low SNR, interference, and highly structured data) and (2) ML-FSK Modulation-Index Estimation for M-FSK signals in moderate to harsh signal environments, with emphasis on estimation of low modulation-index values.  The performance of each algorithm has been evaluated by Monte Carlo simulation using predominantly synthetic data.

    QAM Classification.  Similar to the FSK processing, a general approach has been developed to perform optimum digital-QAM signal-modulation-classification and signal-parameter-estimation based on the maximum-likelihood principle.  Parameters of interest include symbol rate, constellation order, and constellation configuration.

    High-level capabilities:

            • Able to process single signals in noise
            • Able to estimate M-FSK symbol rate, modulation index, and modulation order (number of tones)
            • Able to estimate QAM symbol rate and constellation

    High-level limitations:

            • Techniques have been primarily validated with Monte Carlo simulations using synthetic data only
            • Techniques are computationally intensive, and may require additional optimization/refinement to be practical
            • Algorithms have demonstrated sensitivity to estimates of secondary parameters including noise power, carrier frequency and carrier phase 

    Digital PSK/QAM Classifier Development

    Presently, SSPI is engaged with a customer to develop a classifier specifically to address the monitoring problem of detection and classification of cochannel signals exhibiting any combination of the following modulation types: PSK2, PSK4, PSK8, QAM16, and OQPSK.  The system in development includes band-of-interest detection, filtering, and sample-rate conversion, higher-order cyclic moment detection and estimation, feature grouping, parameter estimation, and automatic signal classification.  The classifier development is part of a larger project that includes joint Viterbi demodulation of the set of cochannel waveforms that have been detected and classified.  A baseline version of the classifier has just been completed in MATLAB, and performance testing with both collected and synthetic data has begun.  Work remains to finalize the classifier design and optimize the performance and configuration. 

    High-level capabilities:

            • Classification of the following modulation types: PSK2, PSK4, PSK8, QAM16, and OQPSK
            • Ability to process cases with 1 to 3 temporally and spectrally overlapped signals
            • Reasonably good performance demonstrated for estimation of carrier frequencies and symbol rates
            • Reasonably good classification performance demonstrated for cochannel lower-order modulations

    High-level limitations:

            • System is still in development, and is at the alpha-prototype stage
            • For a given SINR, classification performance is highly dependent upon data-record length (number of symbols processed)

    GSM Joint Demodulation

    As part of a multi-year program ending in July 2001, SSPI developed and evaluated two broad classes of signal processing algorithms for detection and copy of cochannel and adjacent-channel interfering GSM signals.  The first class of algorithms is designed to exploit cyclostationarity of the GSM waveforms and employs Frequency-Shift (FRESH) filtering technology to achieve signal separation prior to demodulation.  The second class of algorithms performs joint signal demodulation using a computationally efficient modification of the Viterbi algorithm.  Under this program, SSPI also developed and evaluated a GSM RF environment analyzer designed to enumerate received signals from GSM base stations and mobile users, and to estimate key signal parameters required for subsequent signal geolocation and demodulation. Prototype software for all algorithms was delivered at the conclusion of the program.

    Environment Analyzer

    The GSM Environment Analyzer (GSM-EA) is a software application for estimating GSM signal and propagation channel parameters.  The algorithms are designed to perform robust parameter estimation in multipath and dense cochannel environments.

    High-level Capabilities:

            • The GSM-EA has demonstrated the ability to process up to 20 cochannel GSM signals in single-sensor data
            • The system is at the alpha-prototype software stage and is implemented in non-platform-specific, portable C-code 
            • Testing & analyses has been completed with simulated & collected GSM signals
            • The GSM-EA produces estimates of training sequences (TSC), TOA, FOA, Dummy Burst Type, channel model (pulse estimate), and SINR (among others) 

    High-level Limitations:

            • The GSM-EA has not yet been generalized to process the EDGE or GPRS variants
            • The system does not support the GSM frequency hopping mode
            • Input SNR must be at least -6dB to 0dB (dependent upon multipath severity) for reliable detection/estimation of single signal

    FRESH Filtering

    SSPI has developed a family of techniques that use combined FRESH filtering and fractionally spaced equalizer technology to perform low-cost separation of GSM signals in moderate cochannel signal environments. These techniques utilize a training-assisted, iterative block least squares approach, and are designed to jointly exploit spectral redundancy, known training sequences, and the constant modulus property of the GSM signals. Both single- and multiple-sensor realizations have been developed and evaluated.

    High-level Capabilities:

            • The processors have demonstrated the ability to suppress 2M -1 interferers with an M-antenna system 
            • The processors have achieved a 2.5–3.5 dB output SINR improvement with 1 and 2 antenna systems
            • GSM Frame Error Rates can be decreased by a factor of 3-10
            • The algorithms exhibit low computational cost, and can be easily implemented in off-the-shelf processors

    High-level Limitations:

            • The processors have not yet been extended to support EDGE, GPRS, or frequency hopping modes
            • Applicability is limited to signal environments containing a small number of cochannel signals (e.g., < 2M -1 interferers for an M-antenna system)

    MIMO Processing

    SSPI has developed a class of Multiple-Input-Multiple-Output (MIMO) joint maximum likelihood sequence estimation algorithms for performing joint demodulation of cochannel GSM waveforms.  SSPI has developed and evaluated various techniques to reduce the computational complexity at the expense of moderately increased Bit Error Rate (BER).  These cost-reduction techniques include Constrained Spawning (based on known training and data sequences), the Statistical Thinning Algorithm (STA) for enhanced Viterbi survivor reduction, and methods utilizing Per-Survivor Decision Feedback (PSDF).

    High-level Capabilities:

            • Near-real-time copy of 6-CCI GSM signals w/ 10dB SNRs & dual polarized sensor has been demonstrated
            • Computational complexity reduction of 2 to 4 orders of magnitude over conventional Viterbi joint demodulators
            • Adding sensors reduces the required SNR & computational cost for a fixed BER

    High-level Limitations:

            • The processors have not yet been extended to support EDGE, GPRS, or frequency hopping modes
            • The MIMO processor requires accurate information regarding the propagation channel model (multipath/pulse model)

    CDMA Joint Demodulation

    Under a multi-year program ending in November 2006 SSPI developed and evaluated signal processing algorithms for RF environment analysis and multi-user detection (MUD) for CDMA 2G and 2.5G waveforms in highly dense cochannel interference, as seen by overhead collectors.  A detailed and complete mathematical model of the received signal environment was developed, based on the protocol specifications.  A CDMA Environment Analyzer (CDMA-EA) was developed to detect / enumerate base-station sectors and individual users, and to estimate the channel impulse responses, carrier frequencies, and individual Walsh-channel powers.  The family of MUD techniques developed under this program did not require prior knowledge of the PN mask/state and incorporated a prioritization scheme able to process targeted subsets of signals.  Two specific MUD techniques were developed and evaluated including: (1) B-SIC (Block Successive Interference Cancellation) with relaxed ML joint demodulation, and (2) the Iterative Sign Algorithm (ISA) for computationally efficient identification and demodulation of the initial data block.  Under this program, attainable copy performance was characterized by Monte Carlo simulation for a broad range of signal environments (created using a Government furnished signal simulator, CellSim, as well as SSPI’s own environment generator).  

    High-level Capabilities:

            • CDMA signal enumeration & parameter estimation
            • Specification of synchronized matched filters
            • Parameter estimation including sector/pilot enumeration, carrier frequency & phase, PN short code offset and propagation delay, received impulse response (composite Tx filter and channel response), total sector power, per-traffic-channel power, and per-traffic-channel SINR
            • Combined spatial nulling and joint demodulation of IS-95 downlink signals

    High-level Limitations:

            • Software is in MATLAB™ alpha-prototype stage
            • Supports only IS-95 and cdma2000 waveforms, and is not readily extendable to 4G standards
            • Supports downlink processing only
            • Overall performance limited primarily by TCH/PCH detection and estimation (in the CDMA-EA)
            • Non-real-time operation only
            • Algorithms and software have not yet been optimized to reduce computational complexity, run-time, latency, or memory requirements 

    Mixed PSK/QAM Joint Demodulation

    SSPI is currently developing a generalization of the Viterbi Algorithm (VA) that is capable of jointly demodulating two cochannel PSK/QAM signals exhibiting (possibly) distinct modulation types and modulation parameters (e.g., carrier frequencies, symbol rates, etc.).  This development is being performed as part of a two-year non-Government program that began in May 2009. The most challenging aspect of this joint demodulation problem occurs if the symbol rates of the cochannel signals are different.  In this case, the conventional VA is not directly applicable.  To solve this problem, SSPI has developed a novel time-variable trellis structure, based on VA concepts, to perform optimal joint demodulation of signals with different symbol rates.  The demodulation system incorporates initial synchronization and tracking to acquire and maintain lock on signals with unknown and time-varying amplitude, carrier phase and symbol timing.  The demodulator also incorporates optional per-survivor decision feedback and state pruning to minimize the computational cost.

    High-level Capabilities:

            • Currently supports the following signal modulation types: PSK2, PSK4, PSK8, OQPSK, and QAM16
            • Includes an initial synchronization “bootstrap” capability for obtaining initial carrier and symbol clock synchronization, without the use of any known symbol sequences (e.g., pilots, training sequences, etc.) given approximate estimates of the carrier frequencies and symbol rates.
            • Includes a capability for tracking slowly varying carrier frequencies and symbol rates
            • Performs automatic estimation of the pulse types and excess bandwidths associated with each signal

    High-level Limitations:

            • An alpha-prototype software system is implemented in MATLAB™ only
            • Pulse-type estimation is currently limited to raised cosine and root raised cosine pulse types, and does not yet support multipath propagation channels
            • The joint demodulator must be provided with the input signal modulation types, carrier frequencies, symbol rates, and approximate SNRs (this is currently provided by SSPI’s automatic modulation classification technology)
  • 12.2 Key Roles Played by Academic Institutions

    Reaching back farther into the early 1980s, in connection with my work on exploiting cyclostationarity to despread a Direct-Sequence Spread Spectrum signal without using the spreading code (eventually published in the paper [JP14]), Professor Herschel H. Loomis of the Naval Postgraduate School (NPS) in Monterey, California, brought to my attention a class of signal processing problems needing solutions which he suggested may be amenable to techniques based on exploitation of cyclostationarity. This class of problems, referred to as Signal Interception, belongs to the broader field of what is called Signals Intelligence. The focus of applications of my work on cyclostationarity was originally commercial communications systems, a natural outgrowth of my pre-doctoral work at Bell Telephone Laboratories. The signals intelligence problems that SSPI addressed involved primarily radio frequency wireless communications systems, both commercial and military, but was distinct from my earlier work in that the problems addressed arose from the perspective of unintended receivers wanting to extract information from received signals that would be of value for gathering intelligence for the purpose of national defense.

    This gave rise to a collaboration that led to the expansion of my modest consulting services to commercial industry into an incorporated research and development firm, SSPI (Statistical Statistical | adjective Of or having to do with Statistics, which are summary descriptions computed from finite sets of empirical data; not necessarily related to probability. Signal Processing Inc.), consisting initially of a group of M.S. and Ph.D. students working on thesis projects at the University of California, Davis, and later expanding to include post-doctoral employees from UCD and elsewhere. In parallel with this development, there was the development of an academic group at NPS, under the direction of Professor Loomis. Whereas SSPI focused on the development of theory and method for tackling signals intelligence challenges, the NPS group focused more on evaluating specific techniques suggested by the theory and associated methodology. This synergistic relationship continued into the early 1990s. Following the first workshop on cyclostationarity in 1992, held in SSPI’s hometown of Yountville, California, and co-sponsored by four independent funding agencies, the National Science Foundation and the Offices of Research of the Army, Navy, and Air Force, SSPI’s customer base grew substantially. Although this detracted from the extent of collaboration between SSPI and NPS, the NPS effort on evaluating cyclostationarity-exploiting techniques also continued to grow, as illustrated in the chronological list of NPS thesis projects included below, which covers the 26-year period from 1983 to 2009.

    In addition to UCD and NPS, a key role in applications to signals intelligence was also played by the AFIT (US Air Force Institute of Technology), and other institutions as briefly discussed on Page 6.

    UCD Thesis Projects

    • Ph.D. Daniel Bukofzer “Coherent and noncoherent detection of cyclostationary signals in cyclostationary noise.” 1979
    • M.S. Catherine French “Spread Spectrum despreading without the code.” 1984
    • M.S. Chinkang Chen “Spectral correlation of modulated signals.” 1985
    • Ph.D. William Brown “On the theory of cyclostationary signals.” 1987
    • M.S. Stephan Schell “Self-coherence restoral (SCORE): A new approach to blind adaptation of antenna arrays.” 1987
    • M.S. Chad Spooner “Performance evaluation of detectors for cyclostationary signals.” 1988
    • Ph.D. Randy Roberts “Digital architectures for cyclic spectral analysis.” 1989
    • M.S. Robert Calabretta “On cyclic MUSIC algorithms for signal-selective direction estimation.” 1989
    • Ph.D. Brian Agee “The property restoral approach to blind adaptive signal extraction.” 1989
    • Ph.D. Chihkang Chen “Spectral correlation characterization of modulated signals with application to signal detection and source location.” 1989
    • M.S. Teri Archer “Exploitation of cyclostationarity for identifying the Volterra kernels of a nonlinear system.” 1990
    • Ph.D. Stephan Schell “Exploitation of spectral correlation for signal-selective direction finding.” 1990
    • Ph.D. Chad Spooner “Theory and application of higher-order cyclostationarity.” 1992
    • M.S. Grace Yeung, “New methods of cycle detection.” 1993
    • M.S. Peter Murphy, “Performance evaluation of a blind adaptive antenna array in cellular Communications for increasing capacity.” 1993
    • M.S. Gene Fong, “Evaluation of least-squares algorithms for detection and estimation of cyclostationary signals.” 1993
    • M.S. Jeffrey Schenck, “Evaluation of a method for blind adaptive spatial processing.” 1994
    • M.S. Kurt E. Sundstrom, “Time-variant filtering for GSM signal separation.” 1998
    • Ph.D. Mathew A. Mow, “Periodically-time-variant spatio-temporal filtering for improvement of GSM networks.” 1998

    NPS Thesis Projects

    • “Digital Implementation of Cyclic Spectrum Analysis Techniques for the Detection of Signals in Noise,” William Roscoe Tucker, LT USN, Master of Science in Electrical Engineering, September 1983
    • “Detection of Spread Spectrum Communications,” C. A. Laurvick, LCDR USN, Master of Science in Electrical Engineering, June 1984
    • “Performance Evaluation of the S-1 MkIIA Uniprocessor in Conducting Digital Cyclic Spectral Analysis,” Mark Worthington Hartong, LT USN, Master of Science in Computer Science, June 1985, (C)
    • “VHSIC Implementation of Cyclic Spectrum Analysis Algorithm,” Charles Leonard Kanewske, LT USN, Master of Science in Electrical Engineering, June 1985
    • “Detection of Randomly Clocked Direct Sequence Spread Spectrum Signals,” Stephen Fox, Cpt USA, Master of Science in Electrical Engineering, March 1986
    • “Detection of Spread Spectrum Signals in the Presence of Noise and Interference,” Valdemar K. Johnson, LCDR USN, Master of Science in Electrical Engineering, March 1986
    • “On the Theory of Cyclostationary Signals,” William A. Brown, III, Doctor of Philosophy, Electrical Engineering, University of California, Davis, September 1987, (Member of Guidance Committee)
    • “Interference Removal in Cyclic Spectral Analysis,” Charles Rowe, LCDR USN, Master of Science in Electrical and Computer Engineering (Space Engineering), September 1987.
    • “Cyclic Spectral Analysis Architectures,” Curtis Mitchell, LCDR USN, Master of Science in Weapons Engineering, December 1987.
    • “Design and Analysis of a Covert Communications System,” Richard Lockowitz, LT USN, Master of Science in Systems Technology (Telecommunications Systems Management), September 1988.
    • “Microcomputer Implementations of Spectral Correlation Algorithms,” Thomas V. Cole, LT USN, Master of Science in Electrical Engineering, September 1988.
    • “Geolocation of Direct Sequence Spread Spectrum Signals,” Michael Loomis, LT USN, Master of Science in Electrical Engineering, December,1988.
    • “Design and Implementation of a Receiver for Detecting a Multifrequency Quaternary Phase Shift Keying Signal on an Industry Standard Computer,” Terry K. Gantenbeim, LT USN, Master of Science in Electrical Engineering, June 1989, (R)
    • “Detection of Frequency-Hopping Communications Signals,” Gregory F. Mansfield, LCDR USN, Master of Science in Electrical Engineering, June 1989.
    • “Architectures for Digital Cyclic Spectral Analysis,” Randy S. Roberts, Doctor of Philosophy in Electrical Engineering and Computer Sciences, University of California, Davis, CA, September 1989, (C).
    • “Despreading of Spread Spectrum Signals,” Gregory Point, LT USN, Master of Science in Electrical Engineering, June 1990, (C).
    • “Spread Spectrum Implications in Radar,” William A. Hartung, LT USN, Master of Science in Systems Technology (Space Systems Operations), September 1990.
    • “Feasibility of Two Hypothetical Covert Satellite Communications Systems,” Kim, Hong C., LT USN, Master of Science in Systems Technology (Space Systems Operations), Sept. 1991.
    • “A VLSI Design of a Radix4 Floating Point FFT Butterfly,” Zimmer, Michael, LT USN, Master of Science in Electrical Engineering (Space Systems Engineering), Dec. 1991.
    • “Geolocation using a Cyclostationary Time Difference of Arrival Technique,” Timothy A. Benson, LT USN, Master of Science in Electrical Engineering, December 1992, (C).
    • “Design of Spectral Correlation Analyzer Software,” Nancy Carter, LCDR USN, Master of Science in Electrical Engineering, December 1992, (C).
    • Jackson, Kevin L., LCDR USN, “A CMOS VLSI Implementation of a Near Real Time FFT,” Master of Science in Electrical Engineering, September 1994.
    • Radcliffe, Roy M., LCDR USN, “Detection of Spread Spectrum Signals Using Cyclic Spectral Analysis Techniques” Master of Science in Electrical Engineering, December 1994.
    • Bernstein, Raymond, “A Pipelined Vector Processor and Memory Architecture for Cyclostationary Processing,” Ph.D. in Electrical Engineering, December 1995.
    • Jenik, Douglas A., LT USN, “Time-Difference-of-Arrival Estimation Using Cyclostationary Signal Processing Techniques, Parts I & II,” Master of Science in Electrical Engineering, March 1996
    • David Streight, LT USN, “Application of Cyclostationary Signal Selectivity to the Carry-on Multi-platform GPS Assisted Time Difference of Arrival System, ” Master of Science in Electrical Engineering, March 1997
    • Strozzo, Phillip G., LCDR USN, “Detection and Classification of Digital Communications Signals Using Second- and Higher Order Cyclostationary Features (Parts I & II),” Master of Science in Electrical Engineering, June 1998 (C).
    • Mateo, Niels, LT USN, “The Effects of Time Varying Doppler on Cyclic Spectral Analysis,” Master of Science in Electrical Engineering, December 1998 (C).
    • Streight, David, LT USN, “Maximum-Likelihood Estimators for the Time and Frequency Differences of Arrival of Cyclostationary Digital Communications Signals,” Doctor of Philosophy in Electrical Engineering, June 1999.
    • Antonio F. Lima Jr., Captain, Brazilian Air Force, “Analysis Of Low Probability Of Intercept (LPI) Radar Signals Using Cyclostationary Processing,” Master of Science in Systems Engineering-September 2002, (C)
    • Pedro F. Jarpa, Captain, Chilean Air Force, “Quantifying The Differences In Low Probability Of Intercept Radar Waveforms Using Quadrature Mirror Filtering,” Master of Science in Electrical Engineering-September 2002, (C)
    • Crnkovich, Joseph G., “Efficacy of Various Waveforms to Support Geolocation,” MSEE, June 2009 (C)